Overview

Brought to you by YData

Dataset statistics

Number of variables27
Number of observations2472
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory521.6 KiB
Average record size in memory216.1 B

Variable types

Numeric24
Text2
Categorical1

Alerts

D16: Relações Fracionárias e Decimais is highly overall correlated with D19: Juros Simples and 16 other fieldsHigh correlation
D19: Juros Simples is highly overall correlated with D16: Relações Fracionárias e Decimais and 10 other fieldsHigh correlation
D20: Juros Compostos is highly overall correlated with D16: Relações Fracionárias e Decimais and 2 other fieldsHigh correlation
D24: Fatorar e Simplificar Expressões Algébricas is highly overall correlated with D16: Relações Fracionárias e Decimais and 7 other fieldsHigh correlation
D28: Função Polinomial 1º Grau (Algébrica/Gráfica) is highly overall correlated with D50: Teorema de Pitágoras e Relações Métricas and 4 other fieldsHigh correlation
D42: Probabilidade de um Evento is highly overall correlated with D16: Relações Fracionárias e Decimais and 11 other fieldsHigh correlation
D49: Semelhança de Figuras Planas is highly overall correlated with D19: Juros Simples and 3 other fieldsHigh correlation
D50: Teorema de Pitágoras e Relações Métricas is highly overall correlated with D16: Relações Fracionárias e Decimais and 7 other fieldsHigh correlation
D52: Planificações de Poliedros/Corpos Redondos is highly overall correlated with D16: Relações Fracionárias e Decimais and 9 other fieldsHigh correlation
D53: Razões Trigonométricas no Triângulo Retângulo is highly overall correlated with D16: Relações Fracionárias e Decimais and 4 other fieldsHigh correlation
D54: Área de Triângulo por Coordenadas is highly overall correlated with D16: Relações Fracionárias e Decimais and 4 other fieldsHigh correlation
D55: Equação da Reta (2 Pontos/Ponto-Inclinação) is highly overall correlated with D28: Função Polinomial 1º Grau (Algébrica/Gráfica) and 3 other fieldsHigh correlation
D56: Equações de Circunferências is highly overall correlated with D57: Localização de Pontos no Plano CartesianoHigh correlation
D57: Localização de Pontos no Plano Cartesiano is highly overall correlated with D16: Relações Fracionárias e Decimais and 17 other fieldsHigh correlation
D58: Coeficientes da Equação de uma Reta (Geometria) is highly overall correlated with D16: Relações Fracionárias e Decimais and 8 other fieldsHigh correlation
D64: Unidades de Medida (Capacidade e Volume) is highly overall correlated with D16: Relações Fracionárias e Decimais and 9 other fieldsHigh correlation
D65: Perímetro de Figuras Planas is highly overall correlated with D16: Relações Fracionárias e Decimais and 11 other fieldsHigh correlation
D67: Área de Figuras Planas is highly overall correlated with D16: Relações Fracionárias e Decimais and 13 other fieldsHigh correlation
D71: Área da Superfície Total de Sólidos is highly overall correlated with D42: Probabilidade de um EventoHigh correlation
D72: Volume de Sólidos is highly overall correlated with D16: Relações Fracionárias e Decimais and 9 other fieldsHigh correlation
D76: Informações em Listas/Tabelas e Gráficos is highly overall correlated with D16: Relações Fracionárias e Decimais and 11 other fieldsHigh correlation
D78: Medidas de Tendência Central is highly overall correlated with D16: Relações Fracionárias e Decimais and 9 other fieldsHigh correlation
Indicação do Padrão de Desempenho is highly overall correlated with D16: Relações Fracionárias e Decimais and 16 other fieldsHigh correlation

Reproduction

Analysis started2025-06-27 23:40:45.872771
Analysis finished2025-06-27 23:41:25.682029
Duration39.81 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

D16: Relações Fracionárias e Decimais
Real number (ℝ)

High correlation 

Distinct643
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.917921
Minimum0
Maximum97.5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size19.4 KiB
2025-06-27T20:41:25.764195image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.4
Q122.7
median29.95
Q343.5
95-th percentile70.59
Maximum97.5
Range97.5
Interquartile range (IQR)20.8

Descriptive statistics

Standard deviation16.821915
Coefficient of variation (CV)0.48175591
Kurtosis0.48370321
Mean34.917921
Median Absolute Deviation (MAD)8.95
Skewness1.0288135
Sum86317.1
Variance282.97682
MonotonicityNot monotonic
2025-06-27T20:41:25.855600image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 25
 
1.0%
23.2 18
 
0.7%
25.6 17
 
0.7%
20 17
 
0.7%
33.3 17
 
0.7%
21.6 16
 
0.6%
26.9 15
 
0.6%
30.2 15
 
0.6%
27.8 15
 
0.6%
50 15
 
0.6%
Other values (633) 2302
93.1%
ValueCountFrequency (%)
0 1
< 0.1%
3 1
< 0.1%
4.5 1
< 0.1%
5.6 1
< 0.1%
6.3 1
< 0.1%
6.5 2
0.1%
6.7 1
< 0.1%
7.1 1
< 0.1%
7.4 2
0.1%
7.7 1
< 0.1%
ValueCountFrequency (%)
97.5 1
< 0.1%
94.3 1
< 0.1%
92.9 2
0.1%
91.7 1
< 0.1%
89.6 1
< 0.1%
89 2
0.1%
87.9 1
< 0.1%
87.5 1
< 0.1%
86.3 1
< 0.1%
86.2 2
0.1%

D19: Juros Simples
Real number (ℝ)

High correlation 

Distinct464
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.655825
Minimum11.1
Maximum93.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.4 KiB
2025-06-27T20:41:25.942034image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum11.1
5-th percentile24.5
Q130.6
median35.6
Q342.7
95-th percentile58.19
Maximum93.2
Range82.1
Interquartile range (IQR)12.1

Descriptive statistics

Standard deviation10.355004
Coefficient of variation (CV)0.27499076
Kurtosis1.7602823
Mean37.655825
Median Absolute Deviation (MAD)5.7
Skewness1.0922199
Sum93085.2
Variance107.22611
MonotonicityNot monotonic
2025-06-27T20:41:26.023452image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.3 39
 
1.6%
32.7 29
 
1.2%
34.1 19
 
0.8%
28.6 19
 
0.8%
32.8 18
 
0.7%
29.3 18
 
0.7%
35.4 17
 
0.7%
31.1 17
 
0.7%
36.8 17
 
0.7%
29.6 17
 
0.7%
Other values (454) 2262
91.5%
ValueCountFrequency (%)
11.1 2
0.1%
13.3 1
< 0.1%
14 1
< 0.1%
14.3 1
< 0.1%
14.5 1
< 0.1%
14.8 1
< 0.1%
15.4 1
< 0.1%
15.5 1
< 0.1%
15.8 1
< 0.1%
15.9 1
< 0.1%
ValueCountFrequency (%)
93.2 1
< 0.1%
86.9 1
< 0.1%
85.7 1
< 0.1%
81.7 1
< 0.1%
81 1
< 0.1%
76.8 1
< 0.1%
76.6 1
< 0.1%
76.3 1
< 0.1%
76.2 1
< 0.1%
75.4 2
0.1%

D20: Juros Compostos
Real number (ℝ)

High correlation 

Distinct377
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.52407
Minimum0
Maximum92
Zeros4
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size19.4 KiB
2025-06-27T20:41:26.108521image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.9
Q119.5
median23
Q327.4
95-th percentile40.345
Maximum92
Range92
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation8.258035
Coefficient of variation (CV)0.33673184
Kurtosis7.5044972
Mean24.52407
Median Absolute Deviation (MAD)3.8
Skewness1.8968063
Sum60623.5
Variance68.195143
MonotonicityNot monotonic
2025-06-27T20:41:26.200057image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 33
 
1.3%
22 28
 
1.1%
22.4 26
 
1.1%
20 25
 
1.0%
22.5 25
 
1.0%
21.3 25
 
1.0%
21.7 24
 
1.0%
21.1 24
 
1.0%
23.3 24
 
1.0%
23.1 23
 
0.9%
Other values (367) 2215
89.6%
ValueCountFrequency (%)
0 4
0.2%
3.8 1
 
< 0.1%
5.3 1
 
< 0.1%
7.4 1
 
< 0.1%
8 1
 
< 0.1%
8.3 1
 
< 0.1%
8.8 2
0.1%
9.1 2
0.1%
9.4 1
 
< 0.1%
9.5 1
 
< 0.1%
ValueCountFrequency (%)
92 1
< 0.1%
85.7 1
< 0.1%
85.5 1
< 0.1%
69.7 1
< 0.1%
69.5 1
< 0.1%
69.2 1
< 0.1%
68.9 1
< 0.1%
64.6 1
< 0.1%
64 1
< 0.1%
63.6 1
< 0.1%

D24: Fatorar e Simplificar Expressões Algébricas
Real number (ℝ)

High correlation 

Distinct422
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.370024
Minimum4.7
Maximum80.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.4 KiB
2025-06-27T20:41:26.276707image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum4.7
5-th percentile17.1
Q122.4
median26.4
Q332.4
95-th percentile45.9
Maximum80.6
Range75.9
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.136547
Coefficient of variation (CV)0.32204932
Kurtosis2.3528749
Mean28.370024
Median Absolute Deviation (MAD)4.9
Skewness1.2117969
Sum70130.7
Variance83.476491
MonotonicityNot monotonic
2025-06-27T20:41:26.370070image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 39
 
1.6%
25 34
 
1.4%
23.5 24
 
1.0%
23.1 22
 
0.9%
24.7 22
 
0.9%
28.6 21
 
0.8%
21.1 20
 
0.8%
22.2 20
 
0.8%
21.3 20
 
0.8%
25.9 19
 
0.8%
Other values (412) 2231
90.3%
ValueCountFrequency (%)
4.7 1
< 0.1%
4.8 1
< 0.1%
6.1 1
< 0.1%
7.1 1
< 0.1%
7.7 2
0.1%
8 1
< 0.1%
8.3 1
< 0.1%
8.7 1
< 0.1%
9.1 1
< 0.1%
9.7 1
< 0.1%
ValueCountFrequency (%)
80.6 1
< 0.1%
72.5 1
< 0.1%
69.1 1
< 0.1%
68.1 1
< 0.1%
67.9 1
< 0.1%
66.7 1
< 0.1%
65.8 1
< 0.1%
65.1 1
< 0.1%
64.7 1
< 0.1%
64.2 1
< 0.1%
Distinct443
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.495267
Minimum0
Maximum85.7
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size19.4 KiB
2025-06-27T20:41:26.897124image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.8
Q115.775
median20
Q326.6
95-th percentile43.2
Maximum85.7
Range85.7
Interquartile range (IQR)10.825

Descriptive statistics

Standard deviation10.408871
Coefficient of variation (CV)0.46271384
Kurtosis4.5698553
Mean22.495267
Median Absolute Deviation (MAD)5.1
Skewness1.7509323
Sum55608.3
Variance108.3446
MonotonicityNot monotonic
2025-06-27T20:41:26.984980image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 34
 
1.4%
17.6 26
 
1.1%
17.9 20
 
0.8%
16.5 19
 
0.8%
25 19
 
0.8%
16.1 19
 
0.8%
18.8 19
 
0.8%
17.1 19
 
0.8%
15.2 19
 
0.8%
19.4 19
 
0.8%
Other values (433) 2259
91.4%
ValueCountFrequency (%)
0 2
0.1%
1.5 1
< 0.1%
3.2 1
< 0.1%
4.3 2
0.1%
6.4 1
< 0.1%
6.5 1
< 0.1%
6.6 1
< 0.1%
6.8 1
< 0.1%
6.9 2
0.1%
7 2
0.1%
ValueCountFrequency (%)
85.7 1
< 0.1%
85.2 1
< 0.1%
83.3 1
< 0.1%
80.1 1
< 0.1%
71.5 1
< 0.1%
71 1
< 0.1%
70.6 1
< 0.1%
70.3 1
< 0.1%
69.7 1
< 0.1%
69.4 1
< 0.1%
Distinct521
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.200162
Minimum0
Maximum94.9
Zeros9
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size19.4 KiB
2025-06-27T20:41:27.074938image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.8
Q114.2
median19.8
Q327.9
95-th percentile52
Maximum94.9
Range94.9
Interquartile range (IQR)13.7

Descriptive statistics

Standard deviation13.484503
Coefficient of variation (CV)0.58122453
Kurtosis3.6424592
Mean23.200162
Median Absolute Deviation (MAD)6.5
Skewness1.6915847
Sum57350.8
Variance181.83183
MonotonicityNot monotonic
2025-06-27T20:41:27.161574image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.7 22
 
0.9%
16.4 20
 
0.8%
20 18
 
0.7%
12.5 18
 
0.7%
19 17
 
0.7%
17.6 17
 
0.7%
18.8 17
 
0.7%
21.4 16
 
0.6%
14.3 16
 
0.6%
11.6 16
 
0.6%
Other values (511) 2295
92.8%
ValueCountFrequency (%)
0 9
0.4%
1.4 1
 
< 0.1%
1.6 1
 
< 0.1%
1.8 1
 
< 0.1%
2.4 1
 
< 0.1%
2.6 2
 
0.1%
2.7 1
 
< 0.1%
2.8 1
 
< 0.1%
2.9 1
 
< 0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
94.9 1
< 0.1%
91.9 1
< 0.1%
86.5 1
< 0.1%
85.4 1
< 0.1%
85.2 1
< 0.1%
84.9 1
< 0.1%
84.1 1
< 0.1%
83.9 1
< 0.1%
83.6 1
< 0.1%
83.1 1
< 0.1%

D42: Probabilidade de um Evento
Real number (ℝ)

High correlation 

Distinct596
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.867516
Minimum9.5
Maximum96.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.4 KiB
2025-06-27T20:41:27.238301image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum9.5
5-th percentile23.7
Q131.1
median38.8
Q349.1
95-th percentile73.345
Maximum96.5
Range87
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.772904
Coefficient of variation (CV)0.35284882
Kurtosis0.74339342
Mean41.867516
Median Absolute Deviation (MAD)8.7
Skewness1.0099275
Sum103496.5
Variance218.23868
MonotonicityNot monotonic
2025-06-27T20:41:27.331637image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.3 31
 
1.3%
50 21
 
0.8%
28 16
 
0.6%
40 16
 
0.6%
33.9 16
 
0.6%
27.3 15
 
0.6%
31.6 15
 
0.6%
43.8 14
 
0.6%
44.4 14
 
0.6%
42.9 13
 
0.5%
Other values (586) 2301
93.1%
ValueCountFrequency (%)
9.5 1
 
< 0.1%
14.3 3
0.1%
14.8 1
 
< 0.1%
15 1
 
< 0.1%
16.1 1
 
< 0.1%
16.7 2
0.1%
16.8 1
 
< 0.1%
17 1
 
< 0.1%
17.2 1
 
< 0.1%
17.6 1
 
< 0.1%
ValueCountFrequency (%)
96.5 1
< 0.1%
95.2 1
< 0.1%
93.7 1
< 0.1%
92 1
< 0.1%
91.5 2
0.1%
91.1 1
< 0.1%
90.8 1
< 0.1%
90.4 1
< 0.1%
90.1 1
< 0.1%
89.6 1
< 0.1%

D49: Semelhança de Figuras Planas
Real number (ℝ)

High correlation 

Distinct390
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.829895
Minimum0
Maximum73.8
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size19.4 KiB
2025-06-27T20:41:27.421360image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.455
Q124.8
median29.8
Q336.325
95-th percentile45.3
Maximum73.8
Range73.8
Interquartile range (IQR)11.525

Descriptive statistics

Standard deviation8.4072284
Coefficient of variation (CV)0.27269728
Kurtosis1.0256981
Mean30.829895
Median Absolute Deviation (MAD)5.8
Skewness0.64719082
Sum76211.5
Variance70.68149
MonotonicityNot monotonic
2025-06-27T20:41:27.509558image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.3 22
 
0.9%
23.1 19
 
0.8%
23.7 19
 
0.8%
25.9 19
 
0.8%
27.6 19
 
0.8%
26.1 18
 
0.7%
25 18
 
0.7%
28.7 17
 
0.7%
33.7 17
 
0.7%
36.2 16
 
0.6%
Other values (380) 2288
92.6%
ValueCountFrequency (%)
0 2
0.1%
10.5 1
 
< 0.1%
10.8 1
 
< 0.1%
11 1
 
< 0.1%
11.1 1
 
< 0.1%
12.1 3
0.1%
12.2 1
 
< 0.1%
12.5 1
 
< 0.1%
12.9 1
 
< 0.1%
13.2 2
0.1%
ValueCountFrequency (%)
73.8 1
< 0.1%
72.9 1
< 0.1%
66.4 1
< 0.1%
65.6 1
< 0.1%
65.5 1
< 0.1%
64.2 1
< 0.1%
63 1
< 0.1%
62.5 1
< 0.1%
62.2 1
< 0.1%
61.2 1
< 0.1%

D50: Teorema de Pitágoras e Relações Métricas
Real number (ℝ)

High correlation 

Distinct423
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.980866
Minimum0
Maximum83.3
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size19.4 KiB
2025-06-27T20:41:27.600142image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16
Q120.8
median24.8
Q330.9
95-th percentile45.345
Maximum83.3
Range83.3
Interquartile range (IQR)10.1

Descriptive statistics

Standard deviation9.3857078
Coefficient of variation (CV)0.34786533
Kurtosis3.3111447
Mean26.980866
Median Absolute Deviation (MAD)4.7
Skewness1.4674905
Sum66696.7
Variance88.091512
MonotonicityNot monotonic
2025-06-27T20:41:27.680438image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 31
 
1.3%
20 30
 
1.2%
22.2 24
 
1.0%
24.3 21
 
0.8%
20.3 20
 
0.8%
19.7 20
 
0.8%
25.7 20
 
0.8%
25.5 20
 
0.8%
33.3 20
 
0.8%
21.1 20
 
0.8%
Other values (413) 2246
90.9%
ValueCountFrequency (%)
0 1
< 0.1%
5.6 1
< 0.1%
6.8 1
< 0.1%
7.3 1
< 0.1%
7.4 1
< 0.1%
9.2 1
< 0.1%
9.8 1
< 0.1%
10 2
0.1%
10.5 1
< 0.1%
10.7 1
< 0.1%
ValueCountFrequency (%)
83.3 1
< 0.1%
75.9 1
< 0.1%
74.8 1
< 0.1%
73.1 1
< 0.1%
71.7 1
< 0.1%
70.9 1
< 0.1%
70.8 1
< 0.1%
70.6 1
< 0.1%
69.5 1
< 0.1%
69.4 1
< 0.1%
Distinct398
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.143285
Minimum5.6
Maximum87.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.4 KiB
2025-06-27T20:41:27.767862image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum5.6
5-th percentile17.355
Q122.5
median26.6
Q331.6
95-th percentile44.7
Maximum87.9
Range82.3
Interquartile range (IQR)9.1

Descriptive statistics

Standard deviation8.9128164
Coefficient of variation (CV)0.31669425
Kurtosis5.3568956
Mean28.143285
Median Absolute Deviation (MAD)4.5
Skewness1.7039469
Sum69570.2
Variance79.438296
MonotonicityNot monotonic
2025-06-27T20:41:27.846985image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 35
 
1.4%
24.3 27
 
1.1%
33.3 25
 
1.0%
20 23
 
0.9%
28.6 22
 
0.9%
26 22
 
0.9%
26.5 21
 
0.8%
29 20
 
0.8%
28.1 20
 
0.8%
24.5 20
 
0.8%
Other values (388) 2237
90.5%
ValueCountFrequency (%)
5.6 1
 
< 0.1%
5.9 1
 
< 0.1%
7.5 1
 
< 0.1%
7.7 2
0.1%
9.1 1
 
< 0.1%
9.5 1
 
< 0.1%
9.8 1
 
< 0.1%
10.9 1
 
< 0.1%
11.2 1
 
< 0.1%
11.8 4
0.2%
ValueCountFrequency (%)
87.9 1
< 0.1%
82.6 1
< 0.1%
78.2 1
< 0.1%
77.5 1
< 0.1%
72.7 2
0.1%
72.4 1
< 0.1%
72.3 1
< 0.1%
69.9 1
< 0.1%
69.8 1
< 0.1%
69.5 2
0.1%

D52: Planificações de Poliedros/Corpos Redondos
Real number (ℝ)

High correlation 

Distinct575
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.200445
Minimum0
Maximum95.8
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size19.4 KiB
2025-06-27T20:41:27.929245image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34.6
Q145.3
median54.5
Q364.425
95-th percentile78.8
Maximum95.8
Range95.8
Interquartile range (IQR)19.125

Descriptive statistics

Standard deviation13.537588
Coefficient of variation (CV)0.24524419
Kurtosis-0.25346856
Mean55.200445
Median Absolute Deviation (MAD)9.6
Skewness0.2194652
Sum136455.5
Variance183.2663
MonotonicityNot monotonic
2025-06-27T20:41:28.029694image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 28
 
1.1%
47.2 15
 
0.6%
40 14
 
0.6%
66.7 14
 
0.6%
52 13
 
0.5%
54.5 13
 
0.5%
53.8 13
 
0.5%
57.6 12
 
0.5%
57.9 12
 
0.5%
56 12
 
0.5%
Other values (565) 2326
94.1%
ValueCountFrequency (%)
0 1
< 0.1%
9.1 1
< 0.1%
18.9 1
< 0.1%
20 1
< 0.1%
20.3 1
< 0.1%
21.1 1
< 0.1%
21.4 1
< 0.1%
21.9 1
< 0.1%
24.2 1
< 0.1%
25 1
< 0.1%
ValueCountFrequency (%)
95.8 1
< 0.1%
94.9 1
< 0.1%
94.4 1
< 0.1%
92 1
< 0.1%
91.7 1
< 0.1%
91.3 1
< 0.1%
91.2 1
< 0.1%
90.3 1
< 0.1%
89.3 2
0.1%
89 1
< 0.1%

D53: Razões Trigonométricas no Triângulo Retângulo
Real number (ℝ)

High correlation 

Distinct448
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.931877
Minimum0
Maximum92.9
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size19.4 KiB
2025-06-27T20:41:28.114377image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17.055
Q122.3
median27.2
Q332.7
95-th percentile48.79
Maximum92.9
Range92.9
Interquartile range (IQR)10.4

Descriptive statistics

Standard deviation10.044824
Coefficient of variation (CV)0.3471888
Kurtosis3.9718474
Mean28.931877
Median Absolute Deviation (MAD)5.2
Skewness1.509679
Sum71519.6
Variance100.89848
MonotonicityNot monotonic
2025-06-27T20:41:28.199825image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.3 27
 
1.1%
27.3 25
 
1.0%
25 24
 
1.0%
23.5 21
 
0.8%
25.7 21
 
0.8%
22.1 20
 
0.8%
31.7 19
 
0.8%
23.2 19
 
0.8%
21.7 18
 
0.7%
20.7 18
 
0.7%
Other values (438) 2260
91.4%
ValueCountFrequency (%)
0 1
< 0.1%
5.6 1
< 0.1%
6.7 1
< 0.1%
7.3 1
< 0.1%
8 1
< 0.1%
9.1 2
0.1%
9.4 1
< 0.1%
9.5 2
0.1%
9.7 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
92.9 1
< 0.1%
87.6 1
< 0.1%
85.3 1
< 0.1%
84.9 1
< 0.1%
83.8 1
< 0.1%
75 1
< 0.1%
73.8 1
< 0.1%
73.7 1
< 0.1%
71.1 1
< 0.1%
69.9 1
< 0.1%

D54: Área de Triângulo por Coordenadas
Real number (ℝ)

High correlation 

Distinct440
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.894822
Minimum0
Maximum90.8
Zeros6
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size19.4 KiB
2025-06-27T20:41:28.284663image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.5
Q116.675
median20.6
Q326.2
95-th percentile43.7
Maximum90.8
Range90.8
Interquartile range (IQR)9.525

Descriptive statistics

Standard deviation10.225609
Coefficient of variation (CV)0.44663416
Kurtosis5.2207707
Mean22.894822
Median Absolute Deviation (MAD)4.5
Skewness1.8197375
Sum56596
Variance104.56309
MonotonicityNot monotonic
2025-06-27T20:41:28.372650image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 50
 
2.0%
18.2 30
 
1.2%
25 26
 
1.1%
19 25
 
1.0%
15.4 23
 
0.9%
21.2 23
 
0.9%
21.7 22
 
0.9%
17.6 22
 
0.9%
19.1 21
 
0.8%
21.4 21
 
0.8%
Other values (430) 2209
89.4%
ValueCountFrequency (%)
0 6
0.2%
3.1 1
 
< 0.1%
4.2 1
 
< 0.1%
4.3 2
 
0.1%
4.9 1
 
< 0.1%
5 2
 
0.1%
5.1 1
 
< 0.1%
5.2 1
 
< 0.1%
5.3 1
 
< 0.1%
5.4 2
 
0.1%
ValueCountFrequency (%)
90.8 1
< 0.1%
88.5 1
< 0.1%
87.8 1
< 0.1%
82.3 1
< 0.1%
75 1
< 0.1%
72.2 1
< 0.1%
70.1 1
< 0.1%
70 1
< 0.1%
67 1
< 0.1%
66.9 1
< 0.1%

D55: Equação da Reta (2 Pontos/Ponto-Inclinação)
Real number (ℝ)

High correlation 

Distinct425
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.909709
Minimum0
Maximum91.7
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size19.4 KiB
2025-06-27T20:41:28.462940image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.7
Q114.9
median18.25
Q323.7
95-th percentile41.2
Maximum91.7
Range91.7
Interquartile range (IQR)8.8

Descriptive statistics

Standard deviation10.083293
Coefficient of variation (CV)0.48223018
Kurtosis6.8571429
Mean20.909709
Median Absolute Deviation (MAD)3.95
Skewness2.1865436
Sum51688.8
Variance101.67279
MonotonicityNot monotonic
2025-06-27T20:41:28.552544image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.8 30
 
1.2%
16.4 30
 
1.2%
16.7 29
 
1.2%
20 27
 
1.1%
15.3 26
 
1.1%
14.3 26
 
1.1%
17.7 25
 
1.0%
14.4 24
 
1.0%
17.9 23
 
0.9%
15.6 22
 
0.9%
Other values (415) 2210
89.4%
ValueCountFrequency (%)
0 1
< 0.1%
2.4 1
< 0.1%
3.3 1
< 0.1%
3.8 2
0.1%
5.2 1
< 0.1%
5.4 1
< 0.1%
5.6 2
0.1%
5.9 1
< 0.1%
6.5 2
0.1%
6.6 1
< 0.1%
ValueCountFrequency (%)
91.7 1
< 0.1%
89.6 1
< 0.1%
82.6 1
< 0.1%
81.5 1
< 0.1%
74.3 1
< 0.1%
73.1 1
< 0.1%
70.7 1
< 0.1%
69.9 1
< 0.1%
68.8 2
0.1%
67.3 1
< 0.1%

D56: Equações de Circunferências
Real number (ℝ)

High correlation 

Distinct387
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.49644
Minimum0
Maximum78.8
Zeros7
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size19.4 KiB
2025-06-27T20:41:28.643479image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.8
Q117.4
median20.9
Q325.4
95-th percentile38.945
Maximum78.8
Range78.8
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.3567465
Coefficient of variation (CV)0.37146973
Kurtosis5.9195938
Mean22.49644
Median Absolute Deviation (MAD)3.8
Skewness1.7695406
Sum55611.2
Variance69.835212
MonotonicityNot monotonic
2025-06-27T20:41:28.741697image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 37
 
1.5%
20.8 32
 
1.3%
22.4 30
 
1.2%
18.2 29
 
1.2%
18.8 29
 
1.2%
16.7 28
 
1.1%
25 28
 
1.1%
17.6 25
 
1.0%
21.4 24
 
1.0%
21.1 24
 
1.0%
Other values (377) 2186
88.4%
ValueCountFrequency (%)
0 7
0.3%
2.2 1
 
< 0.1%
3.4 1
 
< 0.1%
3.7 1
 
< 0.1%
4.4 1
 
< 0.1%
5.6 1
 
< 0.1%
6.7 1
 
< 0.1%
7.3 1
 
< 0.1%
7.4 1
 
< 0.1%
7.7 3
0.1%
ValueCountFrequency (%)
78.8 1
< 0.1%
77.4 1
< 0.1%
75.8 1
< 0.1%
73.5 1
< 0.1%
68.5 1
< 0.1%
65.2 1
< 0.1%
64.5 1
< 0.1%
63.3 1
< 0.1%
62.6 1
< 0.1%
62.1 1
< 0.1%

D57: Localização de Pontos no Plano Cartesiano
Real number (ℝ)

High correlation 

Distinct558
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.088673
Minimum19
Maximum96.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.4 KiB
2025-06-27T20:41:28.830911image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile35.8
Q144.7
median51.8
Q362
95-th percentile79.045
Maximum96.4
Range77.4
Interquartile range (IQR)17.3

Descriptive statistics

Standard deviation13.008794
Coefficient of variation (CV)0.24050866
Kurtosis-0.014783415
Mean54.088673
Median Absolute Deviation (MAD)8.2
Skewness0.58074652
Sum133707.2
Variance169.22873
MonotonicityNot monotonic
2025-06-27T20:41:28.931678image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 37
 
1.5%
51.6 20
 
0.8%
51.2 17
 
0.7%
45.5 16
 
0.6%
42.2 15
 
0.6%
46.8 14
 
0.6%
42.9 14
 
0.6%
48.4 14
 
0.6%
46.3 13
 
0.5%
48.3 13
 
0.5%
Other values (548) 2299
93.0%
ValueCountFrequency (%)
19 1
< 0.1%
19.1 1
< 0.1%
20.7 1
< 0.1%
24.7 2
0.1%
25 2
0.1%
26.3 2
0.1%
26.9 1
< 0.1%
27.3 1
< 0.1%
27.7 1
< 0.1%
27.8 1
< 0.1%
ValueCountFrequency (%)
96.4 1
< 0.1%
95.2 1
< 0.1%
94.9 1
< 0.1%
94.5 1
< 0.1%
93.6 1
< 0.1%
93.4 1
< 0.1%
93.3 1
< 0.1%
92.6 1
< 0.1%
92.3 1
< 0.1%
92 1
< 0.1%

D58: Coeficientes da Equação de uma Reta (Geometria)
Real number (ℝ)

High correlation 

Distinct448
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.329612
Minimum4.3
Maximum78.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.4 KiB
2025-06-27T20:41:29.021251image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum4.3
5-th percentile16.3
Q121.6
median26.4
Q332.7
95-th percentile47.3
Maximum78.5
Range74.2
Interquartile range (IQR)11.1

Descriptive statistics

Standard deviation9.9017495
Coefficient of variation (CV)0.34951942
Kurtosis2.7384435
Mean28.329612
Median Absolute Deviation (MAD)5.3
Skewness1.3152855
Sum70030.8
Variance98.044643
MonotonicityNot monotonic
2025-06-27T20:41:29.109559image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 28
 
1.1%
20 25
 
1.0%
23.5 23
 
0.9%
21.1 21
 
0.8%
21.6 19
 
0.8%
25.5 19
 
0.8%
25.3 19
 
0.8%
21.4 19
 
0.8%
22.6 19
 
0.8%
25.8 18
 
0.7%
Other values (438) 2262
91.5%
ValueCountFrequency (%)
4.3 1
< 0.1%
4.5 1
< 0.1%
5.6 1
< 0.1%
6.7 1
< 0.1%
7.3 1
< 0.1%
7.7 1
< 0.1%
8.5 1
< 0.1%
8.8 1
< 0.1%
9.3 1
< 0.1%
9.8 1
< 0.1%
ValueCountFrequency (%)
78.5 1
< 0.1%
76.6 1
< 0.1%
75.6 1
< 0.1%
75.5 1
< 0.1%
73.9 1
< 0.1%
73.5 1
< 0.1%
73.4 1
< 0.1%
72.8 1
< 0.1%
71.3 1
< 0.1%
69.5 1
< 0.1%

D64: Unidades de Medida (Capacidade e Volume)
Real number (ℝ)

High correlation 

Distinct432
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.181189
Minimum0
Maximum85.5
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size19.4 KiB
2025-06-27T20:41:29.203970image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.4
Q121.2
median26.7
Q333.3
95-th percentile47.445
Maximum85.5
Range85.5
Interquartile range (IQR)12.1

Descriptive statistics

Standard deviation10.015006
Coefficient of variation (CV)0.35537911
Kurtosis1.9172131
Mean28.181189
Median Absolute Deviation (MAD)5.9
Skewness1.0214089
Sum69663.9
Variance100.30035
MonotonicityNot monotonic
2025-06-27T20:41:29.287400image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 28
 
1.1%
33.3 24
 
1.0%
26.9 22
 
0.9%
20 22
 
0.9%
22.2 21
 
0.8%
26.7 19
 
0.8%
27.8 19
 
0.8%
18.5 18
 
0.7%
29 18
 
0.7%
23.5 17
 
0.7%
Other values (422) 2264
91.6%
ValueCountFrequency (%)
0 3
0.1%
3 1
 
< 0.1%
4.8 1
 
< 0.1%
5 1
 
< 0.1%
6.1 2
0.1%
6.7 2
0.1%
7.1 1
 
< 0.1%
7.9 1
 
< 0.1%
8.3 3
0.1%
8.4 1
 
< 0.1%
ValueCountFrequency (%)
85.5 1
< 0.1%
83 1
< 0.1%
72.8 1
< 0.1%
72.1 1
< 0.1%
71.9 1
< 0.1%
71.7 1
< 0.1%
71.6 1
< 0.1%
70.2 1
< 0.1%
65.9 1
< 0.1%
65.7 1
< 0.1%

D65: Perímetro de Figuras Planas
Real number (ℝ)

High correlation 

Distinct477
Distinct (%)19.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.375566
Minimum0
Maximum83.3
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size19.4 KiB
2025-06-27T20:41:29.370569image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18.1
Q124.1
median28.8
Q336.1
95-th percentile53.8
Maximum83.3
Range83.3
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.90067
Coefficient of variation (CV)0.34742545
Kurtosis1.7045192
Mean31.375566
Median Absolute Deviation (MAD)5.6
Skewness1.1654105
Sum77560.4
Variance118.82462
MonotonicityNot monotonic
2025-06-27T20:41:29.455961image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.3 28
 
1.1%
25 28
 
1.1%
24.7 19
 
0.8%
27.9 18
 
0.7%
28 18
 
0.7%
30.2 18
 
0.7%
26.2 17
 
0.7%
28.8 16
 
0.6%
21.9 16
 
0.6%
28.3 16
 
0.6%
Other values (467) 2278
92.2%
ValueCountFrequency (%)
0 2
0.1%
5.6 2
0.1%
8.3 2
0.1%
8.6 1
< 0.1%
10 1
< 0.1%
10.1 1
< 0.1%
10.3 1
< 0.1%
10.5 2
0.1%
10.8 1
< 0.1%
11.4 1
< 0.1%
ValueCountFrequency (%)
83.3 1
< 0.1%
82.7 1
< 0.1%
82.1 1
< 0.1%
77.3 1
< 0.1%
77.1 1
< 0.1%
76.4 1
< 0.1%
74.1 1
< 0.1%
71.1 1
< 0.1%
70.5 1
< 0.1%
70.4 1
< 0.1%

D67: Área de Figuras Planas
Real number (ℝ)

High correlation 

Distinct505
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.871238
Minimum0
Maximum93.8
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size19.4 KiB
2025-06-27T20:41:29.545564image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.7
Q119.2
median23.85
Q330.9
95-th percentile51.69
Maximum93.8
Range93.8
Interquartile range (IQR)11.7

Descriptive statistics

Standard deviation11.807464
Coefficient of variation (CV)0.43940901
Kurtosis3.1245426
Mean26.871238
Median Absolute Deviation (MAD)5.35
Skewness1.5453308
Sum66425.7
Variance139.41621
MonotonicityNot monotonic
2025-06-27T20:41:29.628965image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 30
 
1.2%
18.8 26
 
1.1%
16.7 22
 
0.9%
19.1 21
 
0.8%
20 20
 
0.8%
20.5 19
 
0.8%
22.4 19
 
0.8%
23.5 18
 
0.7%
20.4 17
 
0.7%
23.8 17
 
0.7%
Other values (495) 2263
91.5%
ValueCountFrequency (%)
0 2
0.1%
2.3 1
< 0.1%
4.2 1
< 0.1%
4.5 1
< 0.1%
5.1 2
0.1%
5.3 2
0.1%
5.6 1
< 0.1%
6.6 1
< 0.1%
6.8 1
< 0.1%
7.4 1
< 0.1%
ValueCountFrequency (%)
93.8 1
< 0.1%
88 1
< 0.1%
86.7 1
< 0.1%
82.9 1
< 0.1%
82.6 1
< 0.1%
80.9 1
< 0.1%
80.4 1
< 0.1%
77.2 1
< 0.1%
73.6 1
< 0.1%
73.3 1
< 0.1%

D71: Área da Superfície Total de Sólidos
Real number (ℝ)

High correlation 

Distinct417
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.879288
Minimum0
Maximum63.7
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size19.4 KiB
2025-06-27T20:41:29.714278image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.6
Q117.1
median22.6
Q329.525
95-th percentile40.4
Maximum63.7
Range63.7
Interquartile range (IQR)12.425

Descriptive statistics

Standard deviation9.129457
Coefficient of variation (CV)0.38231697
Kurtosis0.36998837
Mean23.879288
Median Absolute Deviation (MAD)6
Skewness0.64269141
Sum59029.6
Variance83.346985
MonotonicityNot monotonic
2025-06-27T20:41:29.803467image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.7 29
 
1.2%
25 29
 
1.2%
24.4 20
 
0.8%
20 20
 
0.8%
28.6 19
 
0.8%
14.8 19
 
0.8%
17.4 19
 
0.8%
14 18
 
0.7%
24.3 18
 
0.7%
18.9 17
 
0.7%
Other values (407) 2264
91.6%
ValueCountFrequency (%)
0 2
0.1%
2.2 1
< 0.1%
2.6 1
< 0.1%
2.9 1
< 0.1%
3.2 1
< 0.1%
3.4 1
< 0.1%
3.6 1
< 0.1%
4.3 1
< 0.1%
4.8 1
< 0.1%
5.6 1
< 0.1%
ValueCountFrequency (%)
63.7 1
< 0.1%
63.3 1
< 0.1%
59.7 1
< 0.1%
58.5 1
< 0.1%
55.6 1
< 0.1%
55.4 1
< 0.1%
54.6 1
< 0.1%
53.3 1
< 0.1%
53 1
< 0.1%
52.8 1
< 0.1%

D72: Volume de Sólidos
Real number (ℝ)

High correlation 

Distinct433
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.972775
Minimum0
Maximum75.9
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size19.4 KiB
2025-06-27T20:41:29.892874image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17.4
Q122.8
median27.3
Q333.025
95-th percentile47.445
Maximum75.9
Range75.9
Interquartile range (IQR)10.225

Descriptive statistics

Standard deviation9.2522525
Coefficient of variation (CV)0.31934298
Kurtosis2.3746726
Mean28.972775
Median Absolute Deviation (MAD)5.1
Skewness1.1570888
Sum71620.7
Variance85.604176
MonotonicityNot monotonic
2025-06-27T20:41:29.982148image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 38
 
1.5%
28.6 24
 
1.0%
22.1 24
 
1.0%
26.6 20
 
0.8%
25.9 19
 
0.8%
26.5 19
 
0.8%
33.3 18
 
0.7%
27.3 18
 
0.7%
29.9 18
 
0.7%
28.8 18
 
0.7%
Other values (423) 2256
91.3%
ValueCountFrequency (%)
0 2
0.1%
4.2 1
< 0.1%
4.5 1
< 0.1%
4.9 1
< 0.1%
5.3 2
0.1%
6.3 1
< 0.1%
9.1 1
< 0.1%
9.5 1
< 0.1%
9.8 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
75.9 1
< 0.1%
74.4 1
< 0.1%
71.5 1
< 0.1%
69.2 1
< 0.1%
69.1 1
< 0.1%
69 1
< 0.1%
67.1 1
< 0.1%
66.7 1
< 0.1%
66.1 1
< 0.1%
65.8 2
0.1%

D76: Informações em Listas/Tabelas e Gráficos
Real number (ℝ)

High correlation 

Distinct385
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.833374
Minimum40.6
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.4 KiB
2025-06-27T20:41:30.068742image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum40.6
5-th percentile67
Q176.8
median83.3
Q389.8
95-th percentile97.1
Maximum100
Range59.4
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.1251303
Coefficient of variation (CV)0.11016249
Kurtosis-0.12692227
Mean82.833374
Median Absolute Deviation (MAD)6.5
Skewness-0.34810636
Sum204764.1
Variance83.268004
MonotonicityNot monotonic
2025-06-27T20:41:30.150560image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 26
 
1.1%
84.8 17
 
0.7%
85 16
 
0.6%
83.1 16
 
0.6%
83.9 16
 
0.6%
85.7 16
 
0.6%
88.5 16
 
0.6%
80.9 15
 
0.6%
83.2 15
 
0.6%
77.8 15
 
0.6%
Other values (375) 2304
93.2%
ValueCountFrequency (%)
40.6 1
< 0.1%
42.1 1
< 0.1%
52.2 1
< 0.1%
52.4 1
< 0.1%
54.8 1
< 0.1%
54.9 1
< 0.1%
55 1
< 0.1%
55.3 1
< 0.1%
56.2 1
< 0.1%
56.3 1
< 0.1%
ValueCountFrequency (%)
100 14
0.6%
99.5 1
 
< 0.1%
99.4 3
 
0.1%
99.3 3
 
0.1%
99.2 2
 
0.1%
98.9 5
 
0.2%
98.8 10
0.4%
98.5 9
0.4%
98.4 5
 
0.2%
98.3 9
0.4%

D78: Medidas de Tendência Central
Real number (ℝ)

High correlation 

Distinct531
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.981877
Minimum0
Maximum87
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size19.4 KiB
2025-06-27T20:41:30.249217image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.6
Q126.8
median34.15
Q342.7
95-th percentile60.98
Maximum87
Range87
Interquartile range (IQR)15.9

Descriptive statistics

Standard deviation12.516492
Coefficient of variation (CV)0.34785544
Kurtosis0.8838493
Mean35.981877
Median Absolute Deviation (MAD)7.95
Skewness0.89097159
Sum88947.2
Variance156.66256
MonotonicityNot monotonic
2025-06-27T20:41:30.330229image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 20
 
0.8%
33.8 16
 
0.6%
23.8 16
 
0.6%
23.5 15
 
0.6%
29.5 15
 
0.6%
36.6 15
 
0.6%
36.4 14
 
0.6%
33.3 14
 
0.6%
22.1 14
 
0.6%
26 13
 
0.5%
Other values (521) 2320
93.9%
ValueCountFrequency (%)
0 1
< 0.1%
8.3 1
< 0.1%
10 1
< 0.1%
10.4 1
< 0.1%
11.1 1
< 0.1%
11.3 1
< 0.1%
12.2 1
< 0.1%
12.5 1
< 0.1%
12.8 2
0.1%
12.9 1
< 0.1%
ValueCountFrequency (%)
87 1
< 0.1%
86 1
< 0.1%
85.8 1
< 0.1%
85.3 1
< 0.1%
82.4 1
< 0.1%
81 1
< 0.1%
80.5 1
< 0.1%
80.4 1
< 0.1%
78.8 1
< 0.1%
77.6 1
< 0.1%

Escola
Text

Distinct763
Distinct (%)30.9%
Missing0
Missing (%)0.0%
Memory size19.4 KiB
2025-06-27T20:41:30.479143image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length75
Median length48
Mean length28.039644
Min length11

Characters and Unicode

Total characters69314
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique144 ?
Unique (%)5.8%

Sample

1st rowLIA SIDOU EEM
2nd rowEEM LIA SIDOU
3rd rowEEM LIA SIDOU
4th rowLIA SIDOU EEMTI
5th rowEEFM DOM ALOISIO LORSCHEIDER
ValueCountFrequency (%)
eem 1090
 
9.9%
eefm 678
 
6.2%
de 624
 
5.7%
eeep 424
 
3.9%
jose 243
 
2.2%
maria 192
 
1.7%
da 162
 
1.5%
professor 149
 
1.4%
professora 127
 
1.2%
antonio 110
 
1.0%
Other values (740) 7198
65.5%
2025-06-27T20:41:30.737075image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 10617
15.3%
8525
12.3%
A 7616
11.0%
O 5969
 
8.6%
R 4861
 
7.0%
I 4227
 
6.1%
M 3639
 
5.3%
S 3433
 
5.0%
N 2634
 
3.8%
D 2532
 
3.7%
Other values (18) 15261
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 69314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 10617
15.3%
8525
12.3%
A 7616
11.0%
O 5969
 
8.6%
R 4861
 
7.0%
I 4227
 
6.1%
M 3639
 
5.3%
S 3433
 
5.0%
N 2634
 
3.8%
D 2532
 
3.7%
Other values (18) 15261
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 69314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 10617
15.3%
8525
12.3%
A 7616
11.0%
O 5969
 
8.6%
R 4861
 
7.0%
I 4227
 
6.1%
M 3639
 
5.3%
S 3433
 
5.0%
N 2634
 
3.8%
D 2532
 
3.7%
Other values (18) 15261
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 69314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 10617
15.3%
8525
12.3%
A 7616
11.0%
O 5969
 
8.6%
R 4861
 
7.0%
I 4227
 
6.1%
M 3639
 
5.3%
S 3433
 
5.0%
N 2634
 
3.8%
D 2532
 
3.7%
Other values (18) 15261
22.0%

Indicação do Padrão de Desempenho
Categorical

High correlation 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size19.4 KiB
Crítico
1710 
Muito Crítico
423 
Intermediário
306 
Adequado
 
33

Length

Max length13
Median length7
Mean length8.782767
Min length7

Characters and Unicode

Total characters21711
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCrítico
2nd rowCrítico
3rd rowCrítico
4th rowCrítico
5th rowMuito Crítico

Common Values

ValueCountFrequency (%)
Crítico 1710
69.2%
Muito Crítico 423
 
17.1%
Intermediário 306
 
12.4%
Adequado 33
 
1.3%

Length

2025-06-27T20:41:30.857554image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-27T20:41:30.935025image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
crítico 2133
73.7%
muito 423
 
14.6%
intermediário 306
 
10.6%
adequado 33
 
1.1%

Most occurring characters

ValueCountFrequency (%)
i 3168
14.6%
o 2895
13.3%
t 2862
13.2%
r 2745
12.6%
C 2133
9.8%
í 2133
9.8%
c 2133
9.8%
e 645
 
3.0%
u 456
 
2.1%
423
 
1.9%
Other values (9) 2118
9.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21711
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 3168
14.6%
o 2895
13.3%
t 2862
13.2%
r 2745
12.6%
C 2133
9.8%
í 2133
9.8%
c 2133
9.8%
e 645
 
3.0%
u 456
 
2.1%
423
 
1.9%
Other values (9) 2118
9.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21711
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 3168
14.6%
o 2895
13.3%
t 2862
13.2%
r 2745
12.6%
C 2133
9.8%
í 2133
9.8%
c 2133
9.8%
e 645
 
3.0%
u 456
 
2.1%
423
 
1.9%
Other values (9) 2118
9.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21711
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 3168
14.6%
o 2895
13.3%
t 2862
13.2%
r 2745
12.6%
C 2133
9.8%
í 2133
9.8%
c 2133
9.8%
e 645
 
3.0%
u 456
 
2.1%
423
 
1.9%
Other values (9) 2118
9.8%
Distinct183
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size19.4 KiB
2025-06-27T20:41:31.064381image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length25
Median length23
Mean length8.9126214
Min length3

Characters and Unicode

Total characters22032
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAQUIRAZ
2nd rowAQUIRAZ
3rd rowAQUIRAZ
4th rowAQUIRAZ
5th rowCAUCAIA
ValueCountFrequency (%)
fortaleza 608
 
20.1%
do 144
 
4.8%
norte 92
 
3.0%
maracanau 64
 
2.1%
sobral 64
 
2.1%
caucaia 64
 
2.1%
juazeiro 56
 
1.8%
sao 44
 
1.5%
crato 40
 
1.3%
acarau 32
 
1.1%
Other values (199) 1820
60.1%
2025-06-27T20:41:31.332248image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 4728
21.5%
R 2216
10.1%
O 1836
 
8.3%
E 1680
 
7.6%
T 1368
 
6.2%
I 1352
 
6.1%
U 1016
 
4.6%
L 908
 
4.1%
C 864
 
3.9%
N 768
 
3.5%
Other values (14) 5296
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22032
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 4728
21.5%
R 2216
10.1%
O 1836
 
8.3%
E 1680
 
7.6%
T 1368
 
6.2%
I 1352
 
6.1%
U 1016
 
4.6%
L 908
 
4.1%
C 864
 
3.9%
N 768
 
3.5%
Other values (14) 5296
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22032
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 4728
21.5%
R 2216
10.1%
O 1836
 
8.3%
E 1680
 
7.6%
T 1368
 
6.2%
I 1352
 
6.1%
U 1016
 
4.6%
L 908
 
4.1%
C 864
 
3.9%
N 768
 
3.5%
Other values (14) 5296
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22032
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 4728
21.5%
R 2216
10.1%
O 1836
 
8.3%
E 1680
 
7.6%
T 1368
 
6.2%
I 1352
 
6.1%
U 1016
 
4.6%
L 908
 
4.1%
C 864
 
3.9%
N 768
 
3.5%
Other values (14) 5296
24.0%

Interactions

2025-06-27T20:41:23.731649image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:46.546181image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:48.194643image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:49.653484image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:51.241569image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:52.989244image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:54.547291image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:56.250953image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:57.743266image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:59.525120image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:01.047150image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:02.525795image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:04.314629image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:05.888468image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:07.406126image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:08.950933image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:10.788521image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:12.413501image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:14.051893image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:15.596406image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:17.109549image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:18.979094image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:20.575963image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:22.133364image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:23.799513image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:46.609742image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:48.248359image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2025-06-27T20:41:16.667729image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:18.543663image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:20.114048image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:21.680518image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:23.276204image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:24.867635image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:47.799129image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:49.282248image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:50.871037image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:52.580876image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:54.162558image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:55.870815image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:57.375648image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:58.936503image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:00.668379image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:02.156790image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:03.691764image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:05.488999image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:07.023683image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:08.559651image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:10.396249image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:11.995078image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:13.652719image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:15.215752image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:16.734382image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:18.604150image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:20.179462image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:21.742048image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:23.340502image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:24.932132image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:47.864859image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:49.340163image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:50.929366image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:52.644336image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:54.215802image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:55.934807image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:57.436169image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:59.002434image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:00.724483image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:02.215948image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:03.757935image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:05.554859image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:07.084119image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:08.622667image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:10.457470image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:12.063953image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:13.711132image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:15.278818image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:16.796678image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:18.663244image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:20.241595image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:21.803832image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:23.399725image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:24.989784image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:47.922919image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:49.402828image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:50.981846image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:52.713320image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:54.286108image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:55.994021image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:57.492589image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:59.058552image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:00.786359image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:02.275409image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:03.818622image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:05.614834image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:07.143291image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:08.688616image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:10.521290image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:12.128234image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:13.777074image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:15.335861image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:16.853111image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:18.719482image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:20.307842image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:21.866048image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:23.468542image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:25.049780image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:47.990067image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:49.466581image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:51.054141image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:52.786300image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:54.353072image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:56.060715image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:57.553067image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:59.133839image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:00.852325image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:02.334033image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:03.889374image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:05.681455image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:07.206908image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:08.757852image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:10.587498image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:12.204628image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:13.845776image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:15.401525image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:16.919637image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:18.791305image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:20.375952image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:21.931953image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:23.534982image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:25.117171image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:48.058854image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:49.530170image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:51.114552image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:52.852384image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:54.417541image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:56.129494image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:57.623645image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:59.391907image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:00.919206image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:02.402440image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:03.958031image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:05.755199image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:07.272948image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:08.824765image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:10.654138image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:12.274523image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:13.918620image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:15.472596image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:16.987839image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:18.851852image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:20.444693image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:21.999291image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:23.604938image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:25.184375image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:48.133229image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:49.590802image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:51.168355image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:52.919830image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:54.487182image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:56.196088image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:57.686989image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:40:59.458248image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:00.986384image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:02.470078image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:04.025258image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:05.828226image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:07.344387image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:08.890737image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:10.727030image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:12.345999image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:13.989115image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:15.536412image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:17.056632image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:18.919660image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:20.509501image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:22.067377image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-06-27T20:41:23.673756image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2025-06-27T20:41:31.451412image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
D16: Relações Fracionárias e DecimaisD19: Juros SimplesD20: Juros CompostosD24: Fatorar e Simplificar Expressões AlgébricasD28: Função Polinomial 1º Grau (Algébrica/Gráfica)D40: Raízes de Polinômios e Fatores 1º GrauD42: Probabilidade de um EventoD49: Semelhança de Figuras PlanasD50: Teorema de Pitágoras e Relações MétricasD51: Propriedades dos PolígonosD52: Planificações de Poliedros/Corpos RedondosD53: Razões Trigonométricas no Triângulo RetânguloD54: Área de Triângulo por CoordenadasD55: Equação da Reta (2 Pontos/Ponto-Inclinação)D56: Equações de CircunferênciasD57: Localização de Pontos no Plano CartesianoD58: Coeficientes da Equação de uma Reta (Geometria)D64: Unidades de Medida (Capacidade e Volume)D65: Perímetro de Figuras PlanasD67: Área de Figuras PlanasD71: Área da Superfície Total de SólidosD72: Volume de SólidosD76: Informações em Listas/Tabelas e GráficosD78: Medidas de Tendência CentralIndicação do Padrão de Desempenho
D16: Relações Fracionárias e Decimais1.0000.6150.5270.5920.4220.4190.6090.3790.5850.4510.6370.5870.5910.4880.4970.7530.5700.6760.7050.7130.2880.6260.6800.6410.651
D19: Juros Simples0.6151.0000.4640.4650.2350.3870.6160.5100.4940.4550.6580.3640.3700.3370.3890.5560.4630.5530.6420.5810.3050.5310.6170.4370.526
D20: Juros Compostos0.5270.4641.0000.4570.3180.3130.4450.3100.4310.3050.4280.4420.3830.3660.3980.5120.3990.4340.4700.5000.2830.4160.4800.4050.509
D24: Fatorar e Simplificar Expressões Algébricas0.5920.4650.4571.0000.3290.2070.5330.3100.5160.2490.4530.5230.4440.3850.4250.5710.4130.4180.4750.5280.4670.4240.5460.3770.507
D28: Função Polinomial 1º Grau (Algébrica/Gráfica)0.4220.2350.3180.3291.0000.3120.279-0.1470.5020.3240.0520.3790.4800.5590.3590.4290.5990.2070.2790.4290.0510.2880.1400.5150.504
D40: Raízes de Polinômios e Fatores 1º Grau0.4190.3870.3130.2070.3121.0000.2890.2960.2650.4940.4240.1480.3930.4520.3640.3900.4190.4510.4470.367-0.0840.4630.2970.4220.446
D42: Probabilidade de um Evento0.6090.6160.4450.5330.2790.2891.0000.4410.5870.3700.5950.3760.3320.3840.3810.5760.5000.3810.5020.6090.5500.3770.6060.2660.506
D49: Semelhança de Figuras Planas0.3790.5100.3100.310-0.1470.2960.4411.0000.2210.3030.7170.1860.1540.1050.2330.3130.1190.4870.5160.3550.3390.4490.5510.1270.359
D50: Teorema de Pitágoras e Relações Métricas0.5850.4940.4310.5160.5020.2650.5870.2211.0000.4100.4060.4760.4630.4720.4030.5610.5620.3710.4930.6070.3770.3920.4240.4290.601
D51: Propriedades dos Polígonos0.4510.4550.3050.2490.3240.4940.3700.3030.4101.0000.4420.1560.3100.3720.3000.3790.4800.4230.4890.4390.0200.4390.2950.4020.482
D52: Planificações de Poliedros/Corpos Redondos0.6370.6580.4280.4530.0520.4240.5950.7170.4060.4421.0000.3670.3410.2960.3980.5750.3240.6610.7030.5650.3380.6260.7390.3590.456
D53: Razões Trigonométricas no Triângulo Retângulo0.5870.3640.4420.5230.3790.1480.3760.1860.4760.1560.3671.0000.4950.3620.4280.5720.3330.4530.4680.4690.3740.4310.5130.4630.546
D54: Área de Triângulo por Coordenadas0.5910.3700.3830.4440.4800.3930.3320.1540.4630.3100.3410.4951.0000.5020.4200.5640.4670.4700.4900.4950.1260.4720.3990.5640.508
D55: Equação da Reta (2 Pontos/Ponto-Inclinação)0.4880.3370.3660.3850.5590.4520.3840.1050.4720.3720.2960.3620.5021.0000.3780.4960.5210.3620.4080.4470.1170.3960.3060.4600.503
D56: Equações de Circunferências0.4970.3890.3980.4250.3590.3640.3810.2330.4030.3000.3980.4280.4200.3781.0000.5140.4150.4390.4620.4770.2280.4350.4230.4310.480
D57: Localização de Pontos no Plano Cartesiano0.7530.5560.5120.5710.4290.3900.5760.3130.5610.3790.5750.5720.5640.4960.5141.0000.5600.5810.6340.6400.2770.5590.6920.5970.601
D58: Coeficientes da Equação de uma Reta (Geometria)0.5700.4630.3990.4130.5990.4190.5000.1190.5620.4800.3240.3330.4670.5210.4150.5601.0000.3930.4490.5720.1270.4030.3420.5540.578
D64: Unidades de Medida (Capacidade e Volume)0.6760.5530.4340.4180.2070.4510.3810.4870.3710.4230.6610.4530.4700.3620.4390.5810.3931.0000.6630.5510.1130.6560.6070.5900.542
D65: Perímetro de Figuras Planas0.7050.6420.4700.4750.2790.4470.5020.5160.4930.4890.7030.4680.4900.4080.4620.6340.4490.6631.0000.5970.2230.6440.6460.5830.580
D67: Área de Figuras Planas0.7130.5810.5000.5280.4290.3670.6090.3550.6070.4390.5650.4690.4950.4470.4770.6400.5720.5510.5971.0000.3270.5360.5580.5320.572
D71: Área da Superfície Total de Sólidos0.2880.3050.2830.4670.051-0.0840.5500.3390.3770.0200.3380.3740.1260.1170.2280.2770.1270.1130.2230.3271.0000.0840.416-0.1120.291
D72: Volume de Sólidos0.6260.5310.4160.4240.2880.4630.3770.4490.3920.4390.6260.4310.4720.3960.4350.5590.4030.6560.6440.5360.0841.0000.5540.5870.561
D76: Informações em Listas/Tabelas e Gráficos0.6800.6170.4800.5460.1400.2970.6060.5510.4240.2950.7390.5130.3990.3060.4230.6920.3420.6070.6460.5580.4160.5541.0000.4360.467
D78: Medidas de Tendência Central0.6410.4370.4050.3770.5150.4220.2660.1270.4290.4020.3590.4630.5640.4600.4310.5970.5540.5900.5830.532-0.1120.5870.4361.0000.528
Indicação do Padrão de Desempenho0.6510.5260.5090.5070.5040.4460.5060.3590.6010.4820.4560.5460.5080.5030.4800.6010.5780.5420.5800.5720.2910.5610.4670.5281.000

Missing values

2025-06-27T20:41:25.294016image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-27T20:41:25.547064image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

D16: Relações Fracionárias e DecimaisD19: Juros SimplesD20: Juros CompostosD24: Fatorar e Simplificar Expressões AlgébricasD28: Função Polinomial 1º Grau (Algébrica/Gráfica)D40: Raízes de Polinômios e Fatores 1º GrauD42: Probabilidade de um EventoD49: Semelhança de Figuras PlanasD50: Teorema de Pitágoras e Relações MétricasD51: Propriedades dos PolígonosD52: Planificações de Poliedros/Corpos RedondosD53: Razões Trigonométricas no Triângulo RetânguloD54: Área de Triângulo por CoordenadasD55: Equação da Reta (2 Pontos/Ponto-Inclinação)D56: Equações de CircunferênciasD57: Localização de Pontos no Plano CartesianoD58: Coeficientes da Equação de uma Reta (Geometria)D64: Unidades de Medida (Capacidade e Volume)D65: Perímetro de Figuras PlanasD67: Área de Figuras PlanasD71: Área da Superfície Total de SólidosD72: Volume de SólidosD76: Informações em Listas/Tabelas e GráficosD78: Medidas de Tendência CentralIndicação do Padrão de DesempenhoMunicípioEscola
033.133.820.029.66.425.537.038.321.623.360.817.814.414.018.250.716.725.427.522.825.428.186.425.5CríticoAQUIRAZLIA SIDOU EEM
124.030.920.719.113.610.747.620.425.522.856.237.015.316.814.945.216.515.022.420.332.721.286.226.0CríticoAQUIRAZEEM LIA SIDOU
230.231.923.825.533.338.741.319.017.516.144.431.733.322.225.869.827.033.325.423.417.037.174.640.3CríticoAQUIRAZEEM LIA SIDOU
337.134.119.826.123.326.934.235.320.225.869.640.727.434.515.168.129.537.128.423.512.228.993.850.0CríticoAQUIRAZLIA SIDOU EEMTI
412.436.119.710.213.624.633.740.625.229.658.524.812.321.521.241.722.221.027.220.017.632.182.315.1Muito CríticoCAUCAIAEEFM DOM ALOISIO LORSCHEIDER
515.523.419.427.116.89.429.520.031.628.734.023.712.716.815.937.912.312.613.817.023.613.775.314.4Muito CríticoCAUCAIAEEFM DOM ALOISIO LORSCHEIDER
622.123.421.824.514.315.520.220.325.017.131.415.112.416.814.936.126.612.621.120.515.620.754.921.5Muito CríticoCAUCAIAEEFM DOM ALOISIO LORSCHEIDER
715.933.518.320.917.610.222.725.921.020.439.520.828.619.521.733.017.321.420.814.214.228.157.127.4Muito CríticoCAUCAIAEEM DOM ALOISIO LORSCHEIDER
819.930.724.615.418.622.534.440.713.421.152.821.316.118.413.631.014.328.827.521.118.228.076.923.2Muito CríticoCAUCAIACAIC PROFESSORA FRANCISCA ESTRELA TORQUATO FIRMEZA
936.327.319.031.330.08.734.528.921.628.043.628.121.025.724.347.116.517.120.323.144.826.387.414.1CríticoCAUCAIACAIC PROFESSORA FRANCISCA ESTRELA TORQUATO FIRMEZA
D16: Relações Fracionárias e DecimaisD19: Juros SimplesD20: Juros CompostosD24: Fatorar e Simplificar Expressões AlgébricasD28: Função Polinomial 1º Grau (Algébrica/Gráfica)D40: Raízes de Polinômios e Fatores 1º GrauD42: Probabilidade de um EventoD49: Semelhança de Figuras PlanasD50: Teorema de Pitágoras e Relações MétricasD51: Propriedades dos PolígonosD52: Planificações de Poliedros/Corpos RedondosD53: Razões Trigonométricas no Triângulo RetânguloD54: Área de Triângulo por CoordenadasD55: Equação da Reta (2 Pontos/Ponto-Inclinação)D56: Equações de CircunferênciasD57: Localização de Pontos no Plano CartesianoD58: Coeficientes da Equação de uma Reta (Geometria)D64: Unidades de Medida (Capacidade e Volume)D65: Perímetro de Figuras PlanasD67: Área de Figuras PlanasD71: Área da Superfície Total de SólidosD72: Volume de SólidosD76: Informações em Listas/Tabelas e GráficosD78: Medidas de Tendência CentralIndicação do Padrão de DesempenhoMunicípioEscola
246214.628.221.825.219.914.431.625.626.532.239.019.217.817.424.246.228.418.817.423.713.522.367.830.7Muito CríticoFORTALEZAEEFM SANTO AMARO
246325.132.518.225.718.728.823.225.217.019.456.023.316.812.220.249.821.227.725.815.217.129.178.937.6CríticoFORTALEZAEEFM SANTO AMARO
246447.046.523.632.115.523.164.146.332.932.574.327.326.822.218.565.830.134.635.036.927.333.594.033.5CríticoFORTALEZAEEM LICEU DO CONJUNTO CEARA
246539.541.622.432.119.311.259.629.727.524.552.332.621.715.322.357.227.824.223.830.531.226.389.837.2CríticoFORTALEZAEEM LICEU DO CONJUNTO CEARA
246645.539.528.630.132.413.549.425.034.430.447.024.015.418.121.456.439.225.928.328.217.531.185.846.1CríticoFORTALEZAEEM LICEU DO CONJUNTO CEARA
246743.538.024.525.414.414.737.427.624.027.658.129.421.012.721.152.925.636.530.929.517.827.584.152.7CríticoFORTALEZAEEM LICEU DO CONJUNTO CEARA
246821.937.516.034.616.726.160.034.127.921.248.822.019.229.28.035.512.133.335.016.735.529.490.941.7CríticoFORTALEZAEEF SAO JOSE DO PICI DAS PEDREIRAS
246928.827.116.119.116.44.848.326.221.713.661.318.618.617.710.651.620.035.020.618.320.918.085.523.7CríticoFORTALEZAEEF SAO JOSE DO PICI DAS PEDREIRAS
247040.940.917.231.829.246.740.417.825.823.336.325.633.831.114.360.229.035.318.015.110.027.577.438.5CríticoFORTALEZAEEF SAO JOSE DO PICI DAS PEDREIRAS
247136.345.323.324.315.553.524.733.021.030.349.537.331.616.719.763.712.840.827.530.810.644.084.851.0CríticoFORTALEZAEEF SAO JOSE DO PICI DAS PEDREIRAS